Combined short-term traffic flow forecast model for Beijing Traffic Forecast System

A short-term traffic flow forecasting model is studied for Beijing Traffic Forecast System. From a practical view, a combined forecast model is considered, including Discrete Fourier Transform model, Autoregressive model and Neighborhood Regression model. In order to update weight real-timely, the Bayesian approach is utilized to adjust weights of each sub-model. A large amount of data test is carried out among all sub-models and combined model. It shows advantages of combined model.